scholarly journals Combining data flow and control flow computing

1982 ◽  
Vol 25 (2) ◽  
pp. 207-217 ◽  
Author(s):  
P. C. Treleaven
Keyword(s):  
Data Flow ◽  
Control Flow ◽  
Combining Data ◽  
And Control

scholarly journals A Detection Method for Abnormal Transactions in E-Commerce Based on Extended Data Flow Conformance Checking

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Yadi Wang ◽  
Wangyang Yu ◽  
Peng Teng ◽  
Guanjun Liu ◽  
Dongming Xiang
Keyword(s):  
Anomaly Detection ◽  
Business Processes ◽  
Data Flow ◽  
Detection Algorithm ◽  
Control Flow ◽  
Smart Devices ◽  
Integration Model ◽  
Whole Process ◽  
Mobile Transaction ◽  
And Control

With the development of smart devices and mobile communication technologies, e-commerce has spread over all aspects of life. Abnormal transaction detection is important in e-commerce since abnormal transactions can result in large losses. Additionally, integrating data flow and control flow is important in the research of process modeling and data analysis since it plays an important role in the correctness and security of business processes. This paper proposes a novel method of detecting abnormal transactions via an integration model of data and control flows. Our model, called Extended Data Petri net (DPNE), integrates the data interaction and behavior of the whole process from the user logging into the e-commerce platform to the end of the payment, which also covers the mobile transaction process. We analyse the structure of the model, design the anomaly detection algorithm of relevant data, and illustrate the rationality and effectiveness of the whole system model. Through a case study, it is proved that each part of the system can respond well, and the system can judge each activity of every mobile transaction. Finally, the anomaly detection results are obtained by some comprehensive analysis.

scholarly journals An Optimization Approach for Mining of Process Models with Infrequent Behaviors Integrating Data Flow and Control Flow

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Li-li Wang ◽  
Xian-wen Fang ◽  
Esther Asare ◽  
Fang Huan
Keyword(s):  
Process Model ◽  
Data Flow ◽  
Process Mining ◽  
Control Flow ◽  
Process Models ◽  
Frequent Pattern ◽  
Optimization Approach ◽  
Event Log ◽  
Flow Information ◽  
And Control

Infrequent behaviors of business process refer to behaviors that occur in very exceptional cases, and their occurrence frequency is low as their required conditions are rarely fulfilled. Hence, a strong coupling relationship between infrequent behavior and data flow exists. Furthermore, some infrequent behaviors may reveal very important information about the process. Thus, not all infrequent behaviors should be disregarded as noise, and identifying infrequent but correct behaviors in the event log is vital to process mining from the perspective of data flow. Existing process mining approaches construct a process model from frequent behaviors in the event log, mostly concentrating on control flow only, without considering infrequent behavior and data flow information. In this paper, we focus on data flow to extract infrequent but correct behaviors from logs. For an infrequent trace, frequent patterns and interactive behavior profiles are combined to find out which part of the behavior in the trace occurs in low frequency. And, conditional dependency probability is used to analyze the influence strength of the data flow information on infrequent behavior. An approach for identifying effective infrequent behaviors based on the frequent pattern under data awareness is proposed correspondingly. Subsequently, an optimization approach for mining of process models with infrequent behaviors integrating data flow and control flow is also presented. The experiments on synthetic and real-life event logs show that the proposed approach can distinguish effective infrequent behaviors from noise compared with others. The proposed approaches greatly improve the fitness of the mined process model without significantly decreasing its precision.

A Comparative Study and Algorithmic Analysis of Workflow Decomposition in Distributed Systems

2019 ◽  
Vol 11 (1) ◽  
pp. 71-100
Author(s):  
Ihtisham Ali ◽  
Susmit Bagchi
Keyword(s):  
Distributed Systems ◽  
Comparative Study ◽  
Heterogeneous Computing ◽  
Data Flow ◽  
Control Flow ◽  
Workflow Modeling ◽  
Decomposition Techniques ◽  
Algorithmic Analysis ◽  
Computing Environments ◽  
And Control

Workflow is an essential mechanism for the automation of processes in distributed transactional systems, including mobile distributed systems. The workflow modeling enables the composition of process activities along with respective conditions, data flow and control flow dependencies. The workflow partitioning methods are used to create sub-partitions by grouping processes on the basis of activities, data flow and control flow dependencies. Mobile distributed systems consisting of heterogeneous computing devices require optimal workflow decomposition. In general, the workflow partitioning is a NP-complete problem. This article presents a comparative study and detailed analysis of workflow decomposition techniques based on graphs, petri nets and topological methods. A complete taxonomy of the basic decomposition techniques is presented. A detailed qualitative and quantitative analysis of these decomposition techniques are explained. The comparative analysis presented in this article provides an insight to inherent algorithmic complexities of respective decomposition approaches. The qualitative parametric analysis would help in determining the suitability of workflow applicability in different computing environments involving static and dynamic nodes. Furthermore, the authors have presented a novel framework for workflow decomposition based on multiple parametric parameters for mobile distributed systems.

A Comparative Study and Algorithmic Analysis of Workflow Decomposition in Distributed Systems

2021 ◽  
pp. 2282-2315
Author(s):  
Ihtisham Ali ◽  
Susmit Bagchi
Keyword(s):  
Distributed Systems ◽  
Comparative Study ◽  
Heterogeneous Computing ◽  
Data Flow ◽  
Control Flow ◽  
Decomposition Techniques ◽  
Algorithmic Analysis ◽  
Computing Environments ◽  
Np Complete ◽  
And Control

Workflow is an essential mechanism for the automation of processes in distributed transactional systems, including mobile distributed systems. The workflow modeling enables the composition of process activities along with respective conditions, data flow and control flow dependencies. The workflow partitioning methods are used to create sub-partitions by grouping processes on the basis of activities, data flow and control flow dependencies. Mobile distributed systems consisting of heterogeneous computing devices require optimal workflow decomposition. In general, the workflow partitioning is a NP-complete problem. This article presents a comparative study and detailed analysis of workflow decomposition techniques based on graphs, petri nets and topological methods. A complete taxonomy of the basic decomposition techniques is presented. A detailed qualitative and quantitative analysis of these decomposition techniques are explained. The comparative analysis presented in this article provides an insight to inherent algorithmic complexities of respective decomposition approaches. The qualitative parametric analysis would help in determining the suitability of workflow applicability in different computing environments involving static and dynamic nodes. Furthermore, the authors have presented a novel framework for workflow decomposition based on multiple parametric parameters for mobile distributed systems.

Sign in / Sign up

Export Citation Format

Share Document